Team
PoPS was started in 2018 in the Center for Geospatial Analytics at North Carolina State University in collaboration with USDA-APHIS. It has rapidly grown into a talented team of field scientists, computer and data scientists, and government organizations dedicated to changing the way pests and pathogens are managed.
Development Team

NC State University
Lead developer of PoPS web platform; serves as project team manager, providing technical guidance and operational and strategic oversight
Website
NC State University
Provides high-level leadership, scientific guidance, and strategic oversight
Website
NC State University
Lead developer of the C++ library API and developer of PoPS module for GRASS GIS; simulation of interception of pests at US ports
Website
NC State University
Lead developer of the tangible user interface Tangible Landscape, and developer for PoPS management module and PoPS modules for GRASS GIS
Website
NC State University
Lead front-end developer of PoPS web platform; performs website design, scientific illustration, and video production
Website
NC State University, Ph.D. student
Description

NC State University, Ph.D. student
Description

NC State University, Ph.D. student
Description

NC State University
Description

NC State University, Ph.D. student
Description
Affiliates

USDA-APHIS
Provides insights into Field Operations and Policy needs for co-developing management strategies

NC State University
Connects PoPS forecasting efforts with SAFARIS initiative
Website
NC State University
Head of NC State’s Global Food Security faculty cluster; co-develops new collaborations between the cluster and Center for Geospatial Analytics
Website
NC State University
Using PoPS for modeling the spread of swine disease in North Carolina, incorporating inter-farm movement information
Website
Oregon State University
Studying long-distance pathogen dispersal and using PoPS to modeling spread of diverse systems, including wheat stripe rust and foot and mouth disease
WebsitePast Team Members

Examined efficacy of spatial sampling designs to delimit pest or pathogen population boundaries and management strategies to account for pests’ biological traits

Automating surveillance alerts using citizen scientist data, develops rapid host maps and identifying parcel-level treatment priorities

Examines global transportation networks and their relationship to pest interception at US ports

Generates high-resolution host maps with complete spatial uncertainty estimations using machine learning

Building a machine-readable database of pests’ biological traits and examining global trade networks to understand mechanisms of pest introduction

Ensures clarity of official project reports and publications, publicizes project findings through news writing and social media